title: Individual-based simulation models of HIV transmission: reporting quality and recommendations.
abstract: BACKGROUND
  Individual-based modeling is a growing technique in the HIV transmission and prevention literature, but insufficient attention has been paid to formally evaluate the quality of reporting in this field. We present reporting recommendations for individual-based models for HIV treatment and prevention, assess the quality of reporting in the existing literature, and comment on the contribution of this model type to HIV policy and prediction.
  
  
  METHODS
  We developed reporting recommendations for individual-based HIV transmission mathematical models, and through a systematic search, used them to evaluate the reporting in the existing literature. We identified papers that employed individual-based simulation models and were published in English prior to December 31, 2012. Articles were included if the models they employed simulated and tracked individuals, simulated HIV transmission between individuals in a particular population, and considered a particular treatment or prevention intervention. The papers were assessed with the reporting recommendations.
  
  
  RESULTS
  Of 214 full text articles examined, 32 were included in the evaluation, representing 20 independent individual-based HIV treatment and prevention mathematical models. Manuscripts universally reported the objectives, context, and modeling conclusions in the context of the modeling assumptions and the model's predictive capabilities, but the reporting of individual-based modeling methods, parameterization and calibration was variable. Six papers discussed the time step used and one discussed efforts to maintain internal validity in coding.
  
  
  CONCLUSIONS
  Individual-based models represent detailed HIV transmission processes with the potential to contribute to inference and policy making for many different regions and populations. The rigor in reporting of assumptions, methods, and calibration of individual-based models focused on HIV transmission and prevention varies greatly. Higher standards for reporting of statistically rigorous calibration and model assumption testing need to be implemented to increase confidence in existing and future modeling results.
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Nadia N
authors.initials: NN
authors.lastname: Abuelezam
authors.forename: Kathryn
authors.initials: K
authors.lastname: Rough
authors.forename: George R
authors.initials: GR
authors.lastname: Seage
mesh_terms: D003198:Computer Simulation; D018563:Disease Notification / Q000592:standards*; D015658:HIV Infections / Q000517:prevention & control* / Q000635:transmission*; D006801:Humans; D008954:Models, Biological; D057285:Precision Medicine / Q000379:methods*